
def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int)   ->       int:






							'''simple docstring'''





							if not isinstance(_lowerCamelCase		,    _lowerCamelCase) or number < 0:
														raise ValueError("Input must be a non-negative integer")

							__UpperCamelCase      :  List[Any]       =   0
							while number:
														# This way we arrive at next set bit (next 1) instead of looping
														# through each bit and checking for 1s hence the
														# loop won't run 32 times it will only run the number of `1` times
														number &= number - 1
														count += 1
							return count


if __name__ == "__main__":
		import doctest

		doctest.testmod()

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase    :   List[str]						=			{
    'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[str]						=			[
		    'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'GPTBigCodeForSequenceClassification',
		    'GPTBigCodeForTokenClassification',
		    'GPTBigCodeForCausalLM',
		    'GPTBigCodeModel',
		    'GPTBigCodePreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpt_bigcode import (
				    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTBigCodeForCausalLM,
				    GPTBigCodeForSequenceClassification,
				    GPTBigCodeForTokenClassification,
				    GPTBigCodeModel,
				    GPTBigCodePreTrainedModel,
				)


else:
		import sys

		lowercase    :   Tuple						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)